计算机与数字工程2024，Vol.52Issue(5)：1359-1365,1393,8.DOI:10.3969/j.issn.1672-9722.2024.05.017
改进KCF的尺度自适应目标跟踪算法研究
Research on Scale-Adaptive Object Tracking Algorithm by Improving KCF
摘要
Abstract
The SMAKCF(Scale-Adaptive Multiple-Feature Anti-Occlusion KCF)is proposed to solve the problem that the KCF algorithm can not adapt to the object scale and occlusion when tracking an object.The SMAKCF algorithm optimizes simultane-ously several problems including scale response,feature extraction,and update strategy.To be specific,a fusion feature is put for-ward combing HOG features and CN features efficiently,then a scale estimation filter is added and the APCE criterion is introduced to improve the updating method of the position estimation filter.Besides,an extra detection module is designed for re-detecting the object which is unreliably detected.Experiments are conducted on 50 test video sequences of Benchmark to evaluate the algorithm performance.It is indicated that SMAKCF algorithm can overcome difficulty in the scale change and occlusion of the object,the tracking ability in the long-term object tracking process is enhanced significantly.关键词
核相关滤波/尺度变化/目标遮挡/重检测Key words
kernelized correlation filters/scale variations/object occlusion/re-detecting分类
信息技术与安全科学引用本文复制引用
刘思思,陈忠,徐雪茹,吴亮..改进KCF的尺度自适应目标跟踪算法研究[J].计算机与数字工程,2024,52(5):1359-1365,1393,8.基金项目
民用航天十三五预先研究项目(编号:D040401-w05) （编号:D040401-w05）
国产卫星应急观测与信息支持关键技术项目(编号:B0302)资助. （编号:B0302）
